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DYNAMO

Dynamics of the MJO

SCIENTIFIC PROGRAM OVERVIEW

Project Summary: DYNAMO is the US component of CINDY2011 (Cooperative Indian

Ocean Experiment on Intraseasonal Variability in the Year 2011), an international field

program planned for late 2011 – early 2012 in the tropical central Indian Ocean region.

DYNAMO is motivated by two facts: (i) Current MJO prediction skill is very limited (<

15 day) and it is the lowest at the MJO initiation stage in the Indian Ocean. (ii) The

inability of reproducing the MJO by current state-of-the-art climate models has weakened

our confidence in their fidelity. The ultimate goal of DYNAMO is to expedite progress in

understanding the mechanism for the tropical intraseasonal variability in the Indian

Ocean region and improving our ability to simulate and predict the tropical intraseasonal

variability, with a focus on convective initiation of the Madden-Julian Oscillation (MJO).

DYNAMO consists of four integrated components: field observations, modeling,

analysis, and forecasts. The field campaign is designed to collect in situ observations

necessary to test five working hypotheses that have been developed through a

combination of models and observations. A climate process and modeling team (CPT) is

being formed to efficiently integrate field observation and modeling activities.

Intellectual Merit: DYNAMO is intended to test the importance of several factors to MJO

initiation. These hypothesized factors include convective suppression mechanisms,

shallow convection moistening and heating in transition from convectively suppressed to

active phases of the MJO, zonal asymmetry in MJO convective activity due to zonal

circulations induced by stratiform heating, extratropical and upstream influences on MJO

initiation, and the role of wind-induced fluxes and air-sea interaction. Assessment of

these mechanisms with DYNAMO observations will improve knowledge on specific

processes relevant to cumulus parameterization in global weather prediction and climate

models. Information gained by DYNAMO relevant to the parameterization problem will

include cloud entrainment and detrainment rates, convective precipitation efficiency,

structures and evolution of convective and precipitating systems, and a newly defined

variable moist convective inhibition.

Broader impact: DYNAMO will provide an excellent opportunity to train young

scientists (graduate students and postdoctoral fellows) to face the challenge of studying

complex multi-scale and air-sea interaction problems in the tropical climate system. The

unique observations to be collected by DYNAMO will fill a gap in the existing data

archive and assist the broad research and operations communities in their persistent

efforts of improving numerical models. Improved model simulations and predictions of

the MJO will promote credibility in climate simulations and projection, and enhance our

capacities of delivering climate prediction and assessment products on intraseasonal

timescales for risk management and decision making.

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D. PROJECT DESCRIPTION

1. Introduction

The Madden-Julian Oscillation (MJO, Madden and Julian 1971, 1972) dominates

tropical intraseasonal (20 – 100 days) variability. As it propagates from the Indian Ocean

eastward across the western and central Pacific (Fig. 1), the MJO interacts with many

weather and climate systems and

influences their prediction. The MJO

often spawns tropical cyclones,

modulates their activity in all ocean

basins, including hurricanes near the

Americas (Liebmann and Hendon

1994; Hall et al. 2001; Bessafi and

Wheeler 2006; Frank and Roundy

2006). An example of MJO

modulation on Gulf of Mexico and

Caribbean Sea hurricanes and

tropical storms is given in Fig. 2. The

MJO affects the onset and

intraseasonal fluctuations of the

monsoons and rainfall in general over

Asia (Annamalai and Slingo 2001),

Australia (Hendon and Liebmann

1990), Americas (Higgins and Shi

2001; Lorenz and Hartmann 2001),

and Africa (Poh et al. 2007). As an

effective source of stochastic

forcing, the MJO influences the

onset, intensification, and

irregularity of ENSO (Kessler et al.

1995; Moore and Kleeman 1999; Zhang and Gottschalck 2002; Zavala-Gary et al. 2005).

Tropical large-scale convective centers organized by the MJO excite teleconnection

patterns that emanate into the extratropics (Higgins and Mo

1997) and thereby induce remote fluctuations in rainfall and

temperature (Bond and Vecchi 2003). Some torrential rain

events along the US west coast are directly related to such

teleconnection patterns (Jones 2000), which are also known as

“atmospheric rivers” and “Pineapple Express”.

The MJO also interacts with the North Atlantic Oscillation

(Lin et al. 2009), Arctic Oscillation (Zhou and Miller 2005) and

Antarctic Oscillation (Carvalho et al. 2005). The Indian Ocean

Dipole Zonal Mode, while modulating MJO activity, can be

affected by the MJO through nonlinear air-sea interaction (Rao

et al. 2008). Fluctuations in Indonesian Throughflow, a main

Figure 1 Composite MJO precipitation anomalies in

eight phases (Courtesy of US CLIVAR MJO Working

Group)

Figure 2 Tracks of hurricanes (red) and tropical storms (blue) in MJO westerly

and easterly phases during 1979 – 1999. (After Maloney and Hartmann 2000)

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artery connecting the Pacific and Indian Oceans through the Maritime Continent, are

strongly influenced by the MJO (Waliser et al. 2003; 2004). The global angular

momentum, the Earth’s rotation rate and the length of the day all fluctuate on

intraseasonal timescales because of the MJO (Weikmann et al. 1997). The MJO also

causes intraseasonal perturbations in atmospheric composition, such as ozone, carbon

dioxide, and aerosols (Tian et al. 2007; 2008) and in ocean chlorophyll (Waliser et al.

2005).

Because of the important role played by the MJO in weather and climate, there is an

increasing demand from the society for accurate MJO prediction. Routine operational

MJO prediction is carried out by NOAA/NCEP/CPC with input from other NOAA

organizations and several operational centers around the world (e.g. NCEP, ECMWF,

CMC, BOM, UK Met Office, JMA, CCS; planned to join by CWB, IMD, and US Navy

FNMOC). Their prediction products reach a wide range of end users, including

emergency response organizations (e.g., American and International Red Cross), US

government (e.g., USAID, Forest Service, National Marine Fisheries Service, River

Forecast Centers, NWS Regional Headquarters), and private industry (American Electric

Power, Earth Satellite Corporation, Moore Capital Management, and many more).

Influences of the MJO have apparently penetrated into many sectors of the society.

Current MJO prediction, however, suffers from low skill, particularly at two stages

of its life cycle: when it is initialized in the Indian Ocean and when it is about to

propagate across the Maritime Continent (Fig. 3).

This MJO prediction problem is compounded by

the well known fact that most start-of-the-art

global climate models fail to reproduce the MJO

with fidelity (e.g. Zhang et al. 2006; Kim et al.

2009). Even models that can produce robust MJO

signals over the western Pacific suffer from

insufficient MJO strength over the Indian Ocean

(Benedict and Randall, 2009). Arguably, the MJO

is the keystone of a seamless weather-climate

prediction system (Brunet et al. 2009; Shapiro et al.

2009). The inability of reproducing MJO signals by

climate models used to project future global climate

change (Lin et al. 2006) weakens our confidence in

their fidelity. While deficiencies in cumulus

parameterization schemes are commonly thought of as a major culprit for problems with

MJO simulation and prediction, the root causes for these deficiencies remain uncertain.

The MJO has thus become a common model validation target and its representation is

often taken as a milestone of model improvement and development (e.g., Miura et al.

2007; Benedict and Randall, 2009).

Development and improvement of parameterizations for weather and climate models

critically rely on in situ observations. A lack of in situ observations in the region of the

tropical Indian Ocean has impeded the progress on the study of MJO, especially its

initiation. A field observation campaign in the tropical Indian Ocean region is therefore

urgently needed to expedite the study on the tropical intraseasonal variability with a focus

on the MJO initiation.

Figure 3 Correlation between predicted

(by GFS) and observed Wheeler and

Hendon (2004) MJO indices. The

Phase of MJO corresponds to those in

Fig. 1. (Courtesy of Jon Gottschalck

and Qin Zhang)

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An international field program, CINDY2011 (Cooperative Indian Ocean Experiment

on Intraseasonal Variability in the Year 2011), will take place in the central equatorial

Indian Ocean in late 2011 – early 2012 to collect in situ observations to advance our

understanding of MJO initiation processes and to improve MJO prediction. The US

research, operations and applications communities are poised to join CINDY2011 and

make unique contributions to this international effort on the study of the MJO.

DYNAMO is the program that organizes the US interest of participating in CINDY2011.

DYNAMO will coordinate with its CINDY2011 partners (Japan, Australia, Seychelles,

India, France) and other field programs (AMIE, HARIMAU, PAC3E-SA/7SEAS, ONR

air-sea interaction) that are being planned to take place also in late 2011 – early 2012.

The integrated observation data set from these programs will cover MJO events at

different stages of their life cycle with complimentary observational emphases. The

opportunity to be an integrated part of these coordinated programs to maximize the value

of observational products makes the timing of late 2011 – early 2012 critical for

DYNAMO.

2. Scientific Problems and Hypotheses

MJO initiation in the equatorial Indian Ocean generates atmospheric deep

convection organized into a large-scale center that, in coupling with the large-scale

circulation, propagates eastward into the western Pacific Ocean. A key challenge for the

prediction of the MJO is capturing the initiation of deep convection in this region. The

challenge is magnified by the lack of in situ observations of the atmospheric vertical

structure over the Indian Ocean. As a result, the initiation process is among the least

understood aspects of the MJO.

To a large extent, our current knowledge of the MJO directly from in situ

observations is mainly based on data from the western Pacific, although satellite remote

sensing and global reanalysis products have helped gain substantial insight of MJO

structures. The in situ observations of TOGA COARE in 1992-93 (Webster and Lukas

2003) captured three MJO events over the western Pacific (Lin and Johnson 1996; Yanai

et al. 2000) and the long record of the TAO mooring array provides reliable MJO

statistics at the surface and in the upper ocean across the entire equatorial Pacific (Zhang

and McPhaden 200l; Roundy and Kiladis 2006). The established ARM Tropical Western

Pacific sites at Manus, Nauru, and Darwin have provided long-term observational data of

the surface and atmosphere that have been used to help understand the role of cloud-

radiation-moisture interaction in the MJO (Mather et al, 2007).

In contrast, there is a stunning lack of in situ atmospheric observations covering the

life cycle, including the initiation, of the MJO in the Indian Ocean region. Time series of

air-sea processes in the Indian Ocean from the RAMA array, yet to be completed, are still

limited to date. None of the earlier atmospheric field campaigns in the Indian Ocean, such

as INDOEX (Ramanathan et al. 2006), JASMINE (Webster et al. 2002), MISMO

(Yoneyama et al. 2008), and Vasco-Cirene (Vialard et al. 2009), provided adequate data

to study MJO initiation.

Satellite data and limited sounding observations indicate that the structure of the

MJO and its embedded synoptic-scale perturbations vary in longitude from the Indian

Ocean to the western Pacific (Kiladis et al. 2005). The phase relationship between MJO

low-level westerlies and precipitation is roughly quadrature in the Indian Ocean but

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almost in phase in the western Pacific (Zhang et al. 2005). Profiles of temperature and

moisture from the NCEP/NCAR reanalysis and AIRS satellite differed significantly over

the Indian Ocean, particularly in the boundary layer (Tian et al. 2006), which further

highlights the need for in-situ observations. The large-scale dynamical component of the

MJO may more strongly interact with the convective component to provide a mechanism

to maintain the MJO propagation in the western Pacific (Wang and Rui 1990). Such a

dynamical factor may or may not exist for MJO initiation in the Indian Ocean (Matthews

2008). One unique climatic feature of the Indian Ocean is the Seychelles-Chagos

thermocline ridge (SCTR) above which the mixed layer is shallow. Over the SCTR, SST

perturbations in the intraseasonal frequency band are extraordinarily large (~ 1 – 2K)

(Harrison and Vecchi 2001; Saji et al 2006; Vialard et al. 2009), signaling that the air-sea

interaction process during the MJO initiation stage could be different from that for the

MJO propagation in the western Pacific where the thermocline is much deeper and

intraseasonal SST perturbations generally weaker (! 1K). Upper-ocean mixing is an

essential element in air-sea interaction. Its detailed vertical profile in the equatorial Indian

Ocean with unique shear structures related to the Wyrtki jets (Wyrtki 1973) has,

however, never been systematically observed and analyzed. The same can be said of its

counterpart of turbulence mixing in the atmospheric boundary layer. In short, what we

know about the MJO in the western Pacific cannot always be applied to the Indian Ocean,

and there is a gaping hole in our observations and knowledge of the physical processes

related to the MJO in the Indian Ocean.

Among many unknowns associated with the MJO in the Indian Ocean, its initiation

stands out. Here, MJO initiation is defined as the time when deep convection is organized

into a large-scale center (convective envelope) that begins to move eastward slowly (~ 5

m/s) as the typical MJO circulation pattern known as the Rossby-Kelvin wave couplet

(Rui and Wang 1990) start to emerge. In the absence of in situ observations in the region

of the tropical Indian Ocean, numerical models have been employed in conjunction with

global data assimilation products to elucidate several possible mechanisms for MJO

initiation. The following mechanisms for MJO initiation have been proposed:

(i) slow energy recharge of the atmospheric column due to sea surface fluxes, moisture

advection and convergence, moistening of the lower troposphere by shallow convection

and its dynamical impacts, and radiative cooling (Blade and Hartmann 1993; Kemball-

Cook and Weare 2001; Raymond 2001; Maloney 2009). Ocean heat storage and

discharge may also contribute to this preconditioning process (Sobel and Gildor 2003);

(ii) forcing from extratropical synoptic to intraseasonal perturbations related to Rossby

waves, cold surges, global wind oscillation and eddy momentum transport, etc. (Lau and

Peng 1987; Hsu et al 1990; Lin et al. 2007; Ray et al. 2009);

(iii) forcing from upstream due to previous circumnavigating MJO events (Matthews

2007), and

(iv) dynamical response to tropical and extratropical stochastic processes (alby and

Garcia 1987; Yu and Neelin 1994).

Each of these mechanisms operates under the influence of short-term climate

variability, such as ENSO and IOD.

Currently, no consensus exists on which mechanisms are most important for MJO

initiation. Their common features allow them to be consolidated into two scenarios:

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A. Internal initiation: The MJO is initialized over the tropical Indian Ocean through local

interaction between the large-scale circulation and convective activity that self-

organizes into large-scale patterns through a slow buildup of tropospheric moisture

content, multi-scale interaction, air-sea interaction, or other processes without

interference from the extratropics or upstream.

B. External Initiation: Perturbations from either the extratropics or upstream (west) lead

to changes in the large-scale circulation and/or thermodynamics over the tropical

Indian Ocean. Deep convection subsequently is enhanced and organized into large-

scale patterns that feed back to the large-scale circulation, giving rise to the MJO.

A common aspect of the two scenarios is the essential role of convection-circulation

interaction in MJO development. The primary differences are internal (tropical Indian

Ocean) vs. external (extratropics or upstream) triggering mechanisms. Scenario A places

the internal processes in the tropical Indian Ocean at the center of MJO initiation and its

prediction. In contrast, Scenario B suggests that processes local in the tropical Indian

Ocean are passive or responsive and, from a viewpoint of prediction, the key to MJO

initiation resides outside the tropical Indian Ocean. Also common between the two

scenarios but not explicitly stated is that convective suppression immediately precedes

convective initiation of an MJO event. Scenario A implies that deep convection is

suppressed primarily because in the absence of large-scale forcing the buildup of a

favorable large-scale environment is a slow process. In Scenario B, suppression

mechanisms come from the large-scale circulation (e.g., dry air advection). There is no

reason that MJO initiation and suppression mechanisms described in the two scenarios

could not both be at work. In addition to these two scenarios, whether and how Asian

continental pollution aerosol may interact with clouds and precipitation associated with

MJO initiation are completely open questions.

One of the most challenging issues of the MJO is its scale selection, especially, the

mechanisms that determine its intraseasonal temporal scale. In the context of MJO

initiation, the questions are why deep convection prior to MJO initiation remains isolated

and unorganized on the large scale for a prolonged period (" 20 days) and how organized

deep systems are sustained for a prolonged period (" 10 days) once they are developed.

An equally challenging and related issue is the mechanisms for the transition from

inactive to active phases. These two issues will be discussed under the two MJO initiation

scenarios.

Internal Initiation

Under this scenario, in the absence of pre-existing external influences, all large-

scale motions engaging with the MJO initiation process originate from the tropical Indian

Ocean region. It is well known that the pattern and strength of the tropical large-scale

circulation are very sensitive to diabatic heating, especially its latitudinal location (Gill

1980) and vertical profile (Hartmann et al. 1984). The latitudinal location of large-scale

diabatic heating can be well inferred from satellite observations of precipitation (e.g.,

TRMM) and clouds (e.g., outgoing longwave radiation). Its vertical profiles can be most

accurately estimated from in situ sounding observations, and we know well how it can be

done (e.g. Yanai et al 1977). Recent studies have found that, if global climate models are

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tuned to reproduce reasonably well mean horizontal distributions of precipitation, their

reproductions of vertical profiles of diabatic heating have substantial biases in

comparison to sounding-based estimates (Hagos et al. 2009). DYNAMO, thus, will focus

on the vertical structure of diabatic heating in the context of convection-circulation

interaction during MJO initiation.

There is mounting observational evidence that tropical precipitation is positively

related to column water vapor on a wide range of timescales and over all oceans

(Bretherton et al. 2004). The variability of column water vapor in this relationship comes

mostly from the lower troposphere above the cloud base and leads precipitation (Sobel et

al. 2004; Holloway and Neelin 2009). Such a relationship is presumably established by

entrainment of moist (dry) environmental air into convective plumes to maintain (reduce)

high updraft buoyancy (Brown and Zhang 1997; Raymond 2000). Numerical simulations

have clearly demonstrated the sensitivity of tropical deep convection, either

parameterized or resolved, on tropospheric moisture (Derbyshire et al. 2004; ). The

second focus of DYNAMO in the context of convection-circulation interaction during

MJO initiation is the vertical structure of humidity and moisture advection. Hence,

moisture budgets derived from DYNAMO soundings will be critical to assess the

processes controlling moistening of the atmospheric column in advance of MJO

initiation.

These two focuses, the vertical structures of diabatic heating and moisture, lay the

foundation for the following discussions for the three stages of MJO initiation: prolonged

convective suppression, transition from convectively suppressed to active phases, and

sustained convectively active phase of the MJO.

Convective Suppression

Prior to MJO convective initiation, this phase, even though commonly labeled as

“suppressed” or “inactive”, is characterized by convective systems of all kind: shallow,

congestus, and deep. But these convective systems often appear to be randomly scattered

and isolated. They can be organized, but only into synoptic-scale perturbations, such as

convectively coupled Kelvin waves. The terms “suppressed” and “inactive” therefore

refer to a lack of deep convection organized into a large-scale pattern. The reason for the

absence of large-scale organization of deep convection in an inactive phase of the MJO is

a major gap in our understanding. It has been shown that some numerical models fail to

reproduce the MJO because they cannot maintain a convectively suppressed state in the

tropics: simulated deep convection is too easily triggered, and therefore perpetually too

frequent and too strong (Zhang and Mu 2005). Therefore, understanding convectively

suppressing mechanisms is as important as understanding the processes responsible for

the suppressed to active phase transition during MJO initiation and for the sustained deep

convection in active phases.

Observational, modeling and theoretical studies have suggested two main possible

mechanisms for convective suppression. One is related to convective inhibition (CIN)

and other to dry-air entrainment in the low to mid troposphere.

CIN is normally defined as the amount of energy required to overcome the

negatively buoyant energy in the environment between the surface to the level of free

convection (LFC). Commonly, large CIN is associated with a temperature inversion that

may be caused by adiabatic warming associated with subsidence. During the suppressed

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phase of the MJO, large-scale subsidence can be related to the zonal overturning

circulation of the MJO. Strong sub-cloud layer updraft is needed to overcome CIN and

initiate deep convection (Mapes 2000).

As mentioned earlier, we have recently gained considerable insight to the role of

tropospheric moisture in tropical deep convection generally and in the MJO specifically.

Persistent anomalously dry troposphere during a suppressed phase of the MJO can be a

sufficient reason for the lack of deep convection organized on large scales (ref). While

both the CIN and dry-air entrainment suppressing mechanisms are viable, it is unlikely

that they work in isolation from each other. For example, strong subsidence can both

create an inversion at the boundary layer top as well as an anomalously dry atmosphere

with deleterious effects on convection. However, there has yet to be, however, a measure

that quantifies their combined effect on suppressing deep convection. Such a measure can

be defined from field observations and numerical experiments to assist model diagnostics

and cumulus parameterization improvement. Hereafter, the combined suppressing effect

of stability (CIN) and dry-air entrainment will be referred to as moist CIN, or MCIN.

But MCIN along is insufficient to explain why deep convection is suppressed on

intraseasonal timescales but exists on smaller scales in the inactive phase prior to MJO

initiation. One possible reason for this is a lack of an energy supply that supports multiple

convective systems. Over the tropical open ocean, the main moisture sources for

sustained deep convection in a large-scale region are moisture convergence and surface

evaporation, both depending on the large-scale circulation. Without any pre-existing

large-scale circulation for moisture convergence, surface evaporation might be too slow

to allow deep convective systems to sustain and self-aggregate into a large-scale pattern.

Locally, deep convection can still occur. But it consumes available moisture quickly and

decays quickly because of MCIN. Net vertical moisture fluxes from the boundary layer to

the lower troposphere by shallow convection to prepare for massive deep convective

development may occur on synoptic scales. But on the MJO spatial scale (see discussion

on the transition period below) it is a very inefficient and slow process. This leads to the

first working hypothesis of DYNAMO:

Hypothesis I: Convective suppression prior to MJO initiation is prolonged because, in

the absence of external influences, the moisture source through surface evaporation over

warm sea surface with weak to moderate surface wind is insufficient to support

precipitating systems on the MJO scales. The timescale of the suppressed phase prior to

MJO initiation is determined by the low efficiency of lower-troposphere moistening by

shallow convection alone.

The essence in this hypothesis is the population of clouds. Too many clouds with zero or

low precipitation efficiencies would accelerate lower-tropospheric moistening and lead to

premature onset of deep convection. Too many isolated deep convection would leave

insufficient moisture and energy for convective systems to be organized into large-scale

patterns needed for MJO initiation (see discussion below). Radar observations of

DYNAMO during suppressed phases will provide statistics of cloud populations. The

DYNAMO sounding observations will provide heating and moisture profiles over a

large-scale domain (the sounding-radar array) that measure the aggregated effect of the

embedded cloud population that must be accurately reproduced by cumulus

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parameterizations in global climate models to simulate well the suppressed phases of the

MJO. Characterizing the large-scale atmospheric and oceanic environment (including

surface fluxes) in which these particular cloud population compositions exist will also be

important.

Convective Transition

MJO convective initiation is marked by transition from convectively suppressed to

active periods. Central to the transition are processes that help overcome MCIN on a

spatial scale comparable to the MJO convective envelope. In the absence of non-local

dynamical influences, recent studies have pointed to two candidates for such processes:

moistening of the low-mid troposphere by shallow convection and convection-circulation

interaction through low-level diabatic heating.

Data analyses have repeatedly shown graduate moistening in the lower troposphere

preceding the convectively active phase of the MJO (Kiladis et al. 2005 ). As mentioned

earlier, a moister troposphere is an environment more favorable to deep convection. This

“moisture preconditioning” has been postulated as a necessary step leading to MJO

convectively active phase and even a fundamental mechanism for the MJO (Thayer-

Calder and Randall 2009). In the absence of large-scale moisture convergence, such

moistening can happen only through detrainment of cloud with low or zero precipitation

efficiency.

As discussed earlier, in the absence of pre-existing large-scale influences,

moistening of the lower troposphere can be efficient and slow. There must be sufficient

energy/moisture source to sustain deep convection on intraseasonal timescale over the

MJO spatial scale. This requires a feedback from the large-scale circulation. It has been

suggested from theoretical and modeling perspectives that shallow convection, with its

latent heating concentrated in the lower troposphere when its precipitation efficiency is

high, is effective in inducing surface and low-level wind convergence in spite of its

relatively small amplitude (Mapes ; Fig. 5a). Such surface and low-level convergence

acts as a major moisture and

moist static energy supply that

may support subsequent deep

MJO convection (e.g., Wang,

Maloney). The need of low-

level heating for the MJO has

also been suggested as a

mechanism for its slow phase

speed by reducing the effective

equivalent depth of the

atmosphere (Lau and Peng

1987; Chang and Lim 1988). Its

role in the MJO through interacting with the large-scale circulation has recently been

increasingly recognized (Wu 2003; Zhang and Mu 2005; Li et al. 2009). Models,

however, generally do not simulate shallow convection and congestus populations well

(Inness et al. 2001, Maloney 2009).

The above discussion can be summarized into the following working hypothesis:

Figure 5 Zonal-vertical circulation (arrows) responding to

(a) shallow and (b) deep heating (colors). Vertical axis is

pressure (hPa). Vertical velocity is amplified 50 times.

(From Zhang and Hagos 2009)

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Hypothesis II: Population of shallow convection plays a two-stage role in the transition

period of MJO initiation: when the precipitation efficiency is low, the moistening effect in

the lower troposphere dominates, which slowly creates a favorable condition for deep

convection; when the precipitation efficiency becomes high, the low-level heating effect

dominates, which induces surface and low-level moisture convergence as an energy

source for deep convection and accelerates the initiation process.

Crucial information needed to test this hypothesis is the change in the cloud

population that accompanies a gradual shift of the large-scale conditions. Radar

observations that carefully document statistics of the evolution in cloud structures,

precipitation efficiencies, and diabatic (latent and radiative) heating profiles will be

compared to the large-scale temperature and humidity profiles and areal mean heating

profiles measured the sounding observations. It should be pointed out that, while

radiative heating is usually dwarfed by latent heating when deep convection is strong, the

two can be comparable during the early stage of the transition period. Cloud radiative

heating generally peak in the mid troposphere (e.g., Liebmann and Hendon, Mather) and

is effective in induce low-level convergent flow. Moisture convergence due to the large-

scale circulation measured by the soundings will be compared to surface flux

measurement to quantify fractional contribution from these two energy sources to

convective development during this critical transition period. The aggregated heating and

moistening effects by developing convective system over the sounding-radar domain and

the accompanying surface fluxes must be accurately reproduced in global climate models

to successfully reproduce MJO initiation.

Convective Maintenance and Termination

After deep convective systems are initiated over a large-scale domain, a

immediate challenging problem is how they are sustained over the MJO scales in both

time and space. Again, this is a scale selection problem, which is at the crux of a

successful theory and hypothesis for the MJO and a successful of a numerical simulation

of the MJO. Instability due to coupling between deep convection and the large-scale

circulation (e.g., ), due to interaction among perturbations of different scales (Biello et al

2007; Maloney 2009), and possibly due to air-sea interaction and wind-induced surface

heat exchange (Sobel et al. 2009 ) have been suggested as the core mechanisms for the

MJO. In the context of MJO initiation under Scenario A, two key issues must be

addressed. One is what determines the large-scale response to initiated deep convection

that leads to an MJO event vs. a synoptic-scale wave perturbation, such as the

convectively coupled Kelvin wave, which happens more often than the MJO. The MJO

and convectively coupled equatorial Kelvin wave share many common features (eastward

propagation, albeit with different phase speed, westward vertical tilt, moistening of the

lower atmosphere east of the convective center, etc.). The major differences are their

frequencies, spatial scales, and potential vorticity (PV) structures: the Kelvin wave has

minimum PV, whereas the MJO has large PV (Schubert and Masarik 2006). One possible

clue for distinguishing the two is PV generation by deep convection.

The second key issue is the moisture source that must be in place to sustain deep

convective precipitation on the MJO time and space scales. As discussed before, the main

moisture sources are large-scale low-level moisture convergence and surface evaporation.

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In this regard, shallow convection with high precipitation efficiency is needed again. It is

known that mature tropical mesoscale convective systems are dominated by stratiform

precipitation, whose diabatic heating profiles is top heavy with heating peaks in the upper

troposphere and cooling due to precipitation evaporation in the lower troposphere (Houze

and others). This top-heavy heating profile tends to generate a mid-level inflow and near

surface divergent outflow. If somehow such statiform heating is projected onto the large

scale, the near surface divergent flow would terminate deep convective period

immediately. To sustain deep convective period, the low-level cooling due to stratiform

precipitation must be balanced by low-level heating by shallow convection. When this

balance happens on the large scale, the responding circulation provides a deep low-level

moisture advection and convergence (Fig. 5b) with a strong surface wind component that

enhances surface evaporation. All these act as major moisture supplies to help sustain

deep convective period to the MJO time scale.

Meanwhile, the mid-level inflow due to stratiform heating may play a dual role in

this stage of the MJO. First, adiabatic cooling due to melting and sublimation right below

the freezing level forces the mid-level inflow to sink (Houze). Such mid-level inflow is

mainly from the west due to the stronger response of the Rossby component than the

Kelvin component (Fig. 5b). When the sinking westerly reaches the surface, it enhances

the westerly wind burst of the MJO and associated surface evaporation. It is possible that

downward and upscale westerly momentum transport is another ingredient of the MJO to

sustain its surface moisture supply for deep convection. Meanwhile, however, this mid-

level westerly inflow tends to bring air that is much drier than air in the convective

updraft and thereby tends to reduce the updraft buoyancy at the west side of the MJO

convective center. This zonal asymmetry may help initiate the eastward movement of

MJO convection before the large-scale dynamics presumably responsible for the MJO

eastward propagation is fully established. Based on the above discussions, over a certain time, shallow convection may lose

the competition to deep convection and when this happens, the stratiform-like heating

profiles on the large scale would determinate local convection and forces it to move

eastward because of the zonal asymmetry. This possible sequence of actions can by

summarized into the next DYNAMO working hypothesis:

Hypothesis III: A balance between precipitating shallow, deep, and stratiform

precipitation is needed to sustain convective period over the MJO space scale. In the

absence of pre-existing large-scale influences, the time scale of the active phase of the

MJO is determined by a graduate shift from this balance to a dominance of stratiform

precipitating systems on the MJO spatial scale.

Testing this hypothesis needs the same field observations as for the other two

hypotheses: a careful documentation of the evolution of the cloud population by radar

observations and the aggregated effects of all convective systems on large-scale heating

and moisture profiles by sounding observations. It is paramount for a global climate

model to reproduce the aggregated heating profiles due to the balanced cloud population

in order to simulate sustained convective period over a large-scale domain. Theories and

models that include the cloud population change may help explore the fundamental

mechanisms for the timescale selection.

12

Role of the Upper Ocean

Unique and relevant features of the tropical Indian Ocean are high mean sea surface

temperature, or a warm pool, as in the western Pacific, the Wyrtki jets and the

Seychelles-Chagos thermocline ridge (SCTR). Since neither the detailed vertical structure

nor the long-time-scale variability of these features has been examined, our

understanding of their contributions to MJO initiation and evolution is rudimentary at

best. Near the SCTR, intraseasonal perturbations in SST are much larger than in the

western Pacific (e.g., ), because of the shallow mixed layer. While the means by which

atmospheric deep convection over the warm pool is suppressed on intraseasonal

timescales must be explored (see Hypothesis I), the warm sea surface makes the tropical

Indian Ocean a favorable location for MJO initiation. During convectively suppressed

phase prior to MJO initiation, with relatively low surface wind speed and in the absence

of large-scale moisture convergence, evaporation from the warm sea surface is the

primary moisture source to maintain the humidity content in the boundary layer and

balances the moisture sink into the troposphere by atmospheric shallow convection. This

boundary-layer moisture reservoir serves as a major energy source for convection

development at the beginning of the transition from suppressed to active phase of the

MJO. As atmospheric shallow convection becomes more precipitating over a large area,

the stronger surface westerly response west of the enhanced shallow convection region

(Fig. 5a) tends to cool the surface faster than to the east. This zonal SST asymmetry may

be another local mechanism that helps to push the MJO convective center eastward

before large-scale atmospheric dynamics presumably responsible for the MJO eastward

propagation are established. Because the strength of the Wyrtki jets varies on

intraseasonal timescales associated with the MJO (e.g., ), its associated shear would also.

Thus, turbulent mixing will undoubtedly evolve differently than in the western Pacific

and therefore play a quantitatively different role in air-sea interaction. In this regard, the

next DYNAMO working hypothesis is:

Hypothesis IV: Upper-ocean processes contribute to MJO initiation through maintaining

high SST prior to the initiation and rapid surface cooling during the transition and early

active periods. Turbulent mixing plays a critical role in these because of the shallow

mixed layer associated with the SCTR, the current shear associated with the Wyrtki jets,

and their intraseasonal variability.

Testing Hypothesis IV requires comprehensive knowledge of upper-ocean dynamics and

thermodynamics as well as surface fluxes. What we learned from TOGA COARE serves

as a solid background for DYNAMO investigation on air-sea interaction. But the shallow

mixed layer atop the SCTR and the current shear in the tropical Indian Ocean make it a

unique problem. TOGA COARE data have shown that entrainment at the bottom of the

mixed layer can play a major role in the upper ocean heat budget but not always because

of the existence of the barrier layer and the depth of the thermocline. This may be

different near the SCTR. The shallow thermocline there makes entrainment cooling a

more viable mechanism for the upper ocean heat budget. To what degree mixing process

can be adequately reproduced in global ocean models so that air-sea interaction in MJO

initiation can be correctly simulated in coupled climate models has always been a

13

challenge. A thorough test of Hypothesis IV would help address the controversial issue

on the role of air-sea interaction in the MJO. While some studies showed a benign or

even detrimental role of air-sea coupling to MJO simulations (Hendon 2000; Liess et al

2004; Grabowski 2006), others suggested enhancement of MJO simulations and

prediction by air-sea coupling (Waliser et al. 1999, ). None of these modeling studies

adequately resolves either the SCTR or the Wyrtki jets (if at all), let alone mixing profiles

in the upper ocean. Detailed, high-resolution measurement of upper-ocean vertical

mixing profiles through the entire cycle of MJO initiation in the vicinity of the SCTR is

an essential component of DYNAMO. These measurements will provide the subsurface

fluxes that, in combination with the atmospheric fluxes at the sea surface, govern the SST

variability on intraseasonal timescales.

External Initiation

Pre-existing external influences can make the MJO initiation process quite different

from what discussed under the scenario of Internal Initiation. MCIN can be maintained

by dry-air advection from extratropics and subsidence of a large-scale overturning

circulation associated with deep convective activity at a remote location. The absence of

such external influences would make the local processes more efficient at overcoming

MCIN. Intrusions of extratropical or upstream perturbations can provide lifting, moisture

convergence and other incentives that further help overcome or remove MCIN. But it is

unlikely that external influences would dictate MJO initiation. Many external

perturbations (such as cold surges, extratropical Rossby waves) are transient with

frequencies higher than that of the MJO. They along cannot sustain tropical deep

convection in an active phase of the MJO and cannot explain the selection of the time

scale for the MJO, even though “MJO-like” tropical perturbations have been generated in

dry models by external influences (Lin et al. 2007; Ray et al. 2009). If shallow convective

systems with their low-level heating are essential to sustained energy supply to the MJO,

as discussed earlier, it is not clear how external perturbations may help maintain these

shallow convective systems. It is possible that external influences are effective only when

the local processes make the condition for MJO initiation ripe, and if local processes help

maintain the initial convective anomaly triggered by extratropical processes. It is thus

perhaps essential that convection is sustained on the MJO scales in both time and space

only when the local processes are engaged. This leads to the final hypothesis of

DYNAMO:

Hypothesis V: External (extratropical and upstream) perturbations, with their large-

scale ascents and low-level convergence, play an activating role to accelerate MJO

initiation primed by internal processes.

Testing this hypothesis requires the in situ measurement from the other hypotheses

plus information of external perturbations which will have to be from global reanalyses

or satellite data. This combination of data would allow a detailed diagnosis of how cloud

population changes with the large-scale environment under the influence of external

perturbations.

In summary, quantitatively testing all these hypotheses, evaluating various

14

mechanisms, identifying critical processes, and forming new ideas on MJO initiation

requires an integrated approach involving observations, modeling, theories, analyses, and

forecasts. A systematic testing of these and other hypotheses is a necessary step toward

improving existing, and designing new model parameterization schemes, whose

deficiencies are always considered the culprit for model infidelity. Satellite observations

and global reanalysis products are very useful in providing the information on the large-

scale background for deep convection. Certain variables can, however, be accurately and

simultaneously obtained only from in situ observations. They include surface fluxes,

vertical profiles of diabatic heating and moistening, structures and evolution of cloud and

precipitation systems, upper ocean and atmospheric boundary-layer structure and mixing.

A lack of these in situ observations from the tropical Indian Ocean makes the testing of

the hypotheses extremely difficult.

3. Program Objectives and Components

The overall goal of DYNAMO is to expedite our understanding of MJO initiation

processes and improving our ability to simulate and forecast the MJO. This goal shall be

reached adapting an integrated observation-modeling-analysis-forecasts approach, which

will be described in this section, and in coordination with DYNAMO’s international and

national partner programs, which will be outlined in section 4. The scientific objectives

of DYNAMO are:

(1) to collect in situ observations from the equatorial Indian Ocean region that are

urgently needed to advance our understanding of the processes key to MJO initiation and

to improve their representations in models;

(2) to identify critical deficiencies in current numerical models that are responsible

for the low prediction skill and poor simulations of MJO initiation and to assist the broad

community effort of improving model parameterizations.;

(3) to provide guiding information to enhance MJO monitoring and prediction

capacities that deliver climate prediction and assessment products on intraseasonal

timescales for risk management and decision making over the global tropics.

To achieve its objectives, DYNAMO is composed of four main components: field

campaign, modeling, analysis, and forecast. These components are closely connected.

The DYNAMO field campaign is designed under the guidance provided by the

DYNAMO modeling activities. Measurement will target quantities identified by

modeling exercises as potentially important to MJO initiation and its prediction and

simulation.

3.1 Field Observations

The DYNAMO field campaign is an integrated element of CINDY2011, the 2011-

2012 international field program in the equatorial central Indian Ocean. Thus, it is

appropriate to describe the DYNAMO field campaign together with CINDY2011. The

general plan of CINDY2011 is to conduct an intensive observing period (IOP) for 3 – 4

months in late 2011 (October – December) – early 2012 (January) aiming to capture the

initiation of at least one major MJO event and its full life cycle during the period with

possibly maximum observations. The IOP will be embedded in an extended observing

15

period (EOP) of 6 months or longer that, with reduced observing capacity, will cover

more than one MJO event. The climatological background for the field observations is

provided by the long-term monitoring networks of IndOOs and RAMA. Detailed

descriptions of the DYNAMO field observations are provided in the Experimental

Design Overview (EDO).

3.2 Modeling

Modeling activity has been a cornerstone of DYNAMO. The DYNAMO field

campaign is motivated by

– The low prediction skill of numerical models at the MJO initiation stage;

– The relatively poor reproduction of the MJO in the Indian Ocean even in models that

show some capability of capturing MJO signals in the western Pacific, and

– Hypotheses on MJO initiation processes proposed from model simulations and

experiments.

The DYNAMO modeling component covers both research and operational

models of various configurations. It will be closely connected to a Climate Process and

Modeling Team (CPT) on the MJO to be proposed, which includes PIs from the

DYNAMO modeling group.

3.2.1 Research Modeling

a) Use of models for field campaign design

Numerical experiments have been used to help optimize the field campaign design.

The field campaign of DYNAMO is designed to acquire in situ atmosphere and ocean

observations needed to test the hypotheses developed from numerical models. As an

example, prior work has shown that model convection parameterizations often do not

allow sufficient moistening of the free troposphere to occur before deep convection is

triggered (Derbyshire et al. 2004). Increasing convective sensitivity to free tropospheric

humidity through use of explicit moisture triggers and other methods that account for

moist convective inhibition processes (see Hypothesis I) has been demonstrated to

improve MJO simulations (Wang and Schlesinger 1999, Tokioka et al. 1988). This points

to deficiencies in mixing and closure assumptions in model convective parameterizations

that might explain their poor MJO simulations. Implementing moisture triggers has been

shown to be an effective way to improve model intraseasonal oscillations and their

initiation, but many methods to date have been ad hoc and have degraded other aspects of

the model climate. Hence, a sounding array that produces high vertical and temporal

resolution moisture and temperature profiles is necessary to assess the convective

inhibition process in the MJO initiation region and how model behavior differs from

reality.

As a further example, a realistic simulation of shallow convection and congestus has

been shown to improve tropospheric moistening processes in advance of MJO convective

initiation in support of Hypothesis II (Zhang and Song 2009). Many models clearly are

not able to capture the trimodal structure of convection that is observed (Inness et al.

2001), although the extent to which shallow heating and congestus are important for MJO

maintenance and initiation remain an open question (Maloney 2009) to be tested during

DYNAMO. Models have similarly generated highly suggestive, although not conclusive,

evidence supporting the importance of stratiform heating, air-sea coupling, and external

16

excitation for initiation and maintenance of fledgling MJO events.

The DYNAMO sounding array has thus been designed to produce high quality and

high vertical resolution retrievals of temperature, humidity, horizontal velocity

components, and other fields that allow calculation of apparent heat source (Q1), apparent

moisture sink (Q2), radiative heating (QR), and vertical mass fluxes that will be invaluable

for diagnosing the evolution and role of clouds in advance of MJO initiation. These

soundings accompanied by radar data will be used to assess the convective suppression

mechanisms active before MJO convective events, the population of clouds and their role

in overcoming convective suppression, and the evolution of heating profiles during the

initiation of MJO events. The design of the upper ocean observing strategy for

DYNAMO has been similarly motivated by hypotheses developed from coupled models.

b) Diagnostic development for model improvement

Careful evaluation of the field observations as well as complementary data sets, such

as from satellite and the YOTC virtual field campaign, can be used to evaluate models

and improve parameterizations. How to best utilize observations to both gain a deeper

understanding of the processes and guide model improvements is a difficult and complex

task. DYNAMO modelers are working to develop improved and/or targeted diagnostics

tuned to the MJO initiation process that can be applied across all models. These should

include diagnostics that test the hypothesized contributions of shallow convection (and

other vertical modes), vertical and horizontal advection, and surface fluxes in priming the

atmosphere for an MJO convective event, as well as a suite of similar common

diagnostics focused on triggering. DYNAMO field observations have been designed such

that model diagnostics can be directly compared to those from the sounding array. As an

example, from the DYNAMO IOP and complementary radar data, it can be determined

how cloud populations vary as a function of large-scale environment in observations and

models. Do convectively suppressed regimes exist that are dominated by shallow clouds

in both models and observations? If the models do not show this, it would suggest that

moist convective inhibition is not being properly simulated in models and requires

improvement. Maybe convective plumes in models are too undiluted and hence not

responsive to environmental conditions? How do entrainment and detrainment profiles

from the model compare to what is obtained from the sounding array for similar

environmental conditions (Yanai and Johnson 1993)? Judicious use of single column

modeling and CRMs will aid these model-observation comparisons.

The role of clouds in moisture and moist static energy budgets are a common thread

in many of the hypotheses for MJO initiation presented above. Recent model budget

diagnoses have assessed the role that clouds, horizontal advection, surface fluxes, and

radiative feedbacks have played in moistening and energizing the column in models

(Benedict and Randall 2009, Maloney 2009). Budgets derived from the DYNAMO

sounding array can for example suggest which models are properly simulating the role of

shallow convection in the moisture budget (possibly in advance of MJO convection), and

if not, suggest that shallow convective processes need improvement. Targeted

diagnostics for MJO initiation to assess coupled model performance can be developed in

a similar way to those for atmospheric measurements, and these diagnostics should at

minimum include SST, ocean heat content, mixed layer depth, barrier layer thickness,

and surface heat and radiative fluxes.

17

Diagnostic development in advance of the DYNAMO field phase will also be aided

by leveraging existing external efforts. For example, the undertaking of a “virtual field

campaign” through YOTC will aid development of advanced diagnostics that can be used

for model comparison and hypothesis testing. The challenge of how best utilize a

hierarchy of models to gain process understanding of MJO initiation and also improve

parameterization is common to both YOTC and DYNAMO, and thus DYNAMO has

much to learn from YOTC efforts at such integration. Key as well will be use of CRMs

to complement the observational datasets to aid improvement of models with

parameterized convection.

c) The DYNAMO Model Working Group and Ties to External Efforts

The DYNAMO modeling group brings to the table an impressive hierarchy of

models, including non-hydrostatic tropical channel models, cloud resolving models,

global and regional operational forecast and research models in various atmospheric,

oceanic, and coupling configurations. A DYNAMO modeling working group was formed

by scientists from national modeling centers and universities to coordinate modeling

activities contributing to the DYNAMO objectives. These participants are listed in Table

1. To further tighten the connection between DYNAMO field observations, modeling,

and model improvement, a climate process and modeling team (CPT) is being formed in

response to the announcement of 2009 NOAA Climate Program Funding Opportunities,

whose letter of intent has been submitted. There is substantial overlap of participants in

the DYNAMO modeling working group and this proposed CPT effort. This CPT, to be

led by Eric Maloney, Duane Waliser, and Chidong Zhang, will partner with DYNAMO

to carry out many of its modeling improvement tasks, particularly as they relate to MJO

initiation in the Indian Ocean. In this CPT, three national modeling centers (NCEP/EMC,

NCAR, NASA/GSFC) will team up with PIs from seven universities, to systematically

work on model cumulus parameterization problems using the MJO as a target to identify

deficiencies in models. The CPT effort will also leverage existing NOAA and NASA

projects led by Adam Sobel to improve MJO simulation in global models at GFDL and

NASA/GISS. Dedicated postdoctoral researchers will be located at each participating

modeling center with the sole task of engendering parameterization improvements that

will produce more realistic simulations of MJO initiation and maintenance, with the goal

of engendering improvements that can be universally applied across models and not be ad

hoc. The models to be involved are known to span a wide range of MJO simulation

capability (from little MJO signals to robust MJO signals with unrealistic distributions).

This CPT will work closely with the WCRP/WWRP MJO Task Force to develop and

demonstrate a new set of process-oriented MJO diagnostics that emphasize the vertical

structure of the MJO in terms of its diabatic heating, moistening, cloud and precipitation

structure and evolution, diagnostics suitable for testing the DYNAMO hypotheses

presented above. The DYNAMO field campaign will provide unique observations on

these quantities in the context of MJO initiation. Recent satellite (TRMM, A-Train) data

will be used to derive global statistics and the DYNAMO/CINDY2011 field observations

will be used to validate and calibrate the global statistics. After diagnostics are developed

and parameterization improvements are proposed on the basis of this diagnostic

evaluation, the CPT will conduct coordinated numerical experiments with common

parameterization sensitivity tests and modifications across all models involved. These

18

experiments will reveal physical processes (e.g., convective entrainment and detrainment

rates, closure assumptions, triggering, momentum transport, and autoconversion) whose

treatment perturbation might lead to similar sensitivities in the reproduction of the MJO

signals by models with different configurations. DYNAMO field observations will be

used to constrain and validate simulations by single column versions of the global

models, which will also be used in common sensitivity experiments. The outcome of this

CPT will be targeted information for parameterization improvement for each

participating model and a guideline of common procedures to conduct diagnoses and

sensitivity tests again observations to identify the root causes of model parameterization

deficiencies that lead to failure of MJO reproduction with fidelity.

Table 1 Core DYNAMO Modeling Working Group

PI Affiliation Modeling Expertise

Sue Chen NRL/Monterey regional coupled forecast model

(COAMPS)

Maria Flatau NRL/Monterey global coupled forecast model

(NOGAPS)

Tim Li UH global and regional research models

Eric Maloney CSU climate model (CCSM)

Mitch Moncrieff NCAR MMM cloud resolving and tropical channel

models

Justin Small NRL/Stennis regional coupled models

Augustin Vintzileos NCEP/EMC global coupled forecast model (CFS)

Duane Waliser NASA/JPL Global and regional models

Xiaoqing Wu ISU cloud resolving and climate models

and parameterization development

Guang Zhang SIO climate models and parameterization

development

3.2.2 Forecast Modeling

Low skill in predicting the MJO serves as a major motivation for DYNAMO.

Improving MJO prediction will be a major testament of the legacy of DYNAMO.

Operational forecast and reforecast (hindcast) and their validations provide data that,

when wisely diagnosed, may reveal root causes for the low prediction skill. Based on the

preliminary results from CAPT, model deficiencies found in forecast/reforecast and

climate simulations of the MJO should converge. If so, commonly identified model

deficiencies would lead to high priority targets for model improvement and also help set

priorities for future in-situ and satellite observations.

In addition to global forecast, it would also be desirable to have forecast based on

high-resolution regional models with boundary conditions from global models. For such

high-resolution limited area forecast, different metrics to evaluate its success need to be

established. Extended-range reforecasts (beyond 15 days) and recovery from archived

real-time operational forecasts should be used to establish the baseline forecast statistics

to measure future improvement in MJO prediction.

We expect that the forecast products relevant to DYNAMO will be multivariate.

19

Reforecasting and its verification will help categorize model performance in terms of

variables, initial conditions, lead time, and MJO phases. Participation of multiple models

in the forecast and reforecast exercises will help formulate strategies of multimodel

ensemble forecast of the MJO. With field observations at hand, data denial experiments

can be done to further identify sensitivities of forecast to certain variables at certain

geographical locations and MJO phases. These would be complementary to the

hypothesis testing using research models as described in the previous subsection.

So far, three national forecast/modeling centers plan to participate in the DYNAMO

forecast/reforecast exercises. They are NOAA (CFS), NRL (COAMPS and NOGAPS),

and NASA (GEOS5).

3.3. Analysis

It is extremely important to provide a global and long-term context to the limited

DYNAMO observational coverage in both time and space. This must be done through

using existing global data sets. The analysis component of DYNAMO will compare

DYNAMO field observations to available satellite and mooring data and reanalysis

products and to quantify the degree to which these data can be used to describe the

atmospheric and oceanic structures and processes relevant to MJO initiation. Long-term

statistics based on these data will be compared to case studies based on DYNAMO field

observations. Particularly useful in this regard are data from TRMM, A-TRAIN

(especially CloudSat, AIRS, CALIPSO), and RAMA that provide vertical structures of

the atmosphere and ocean. The utility of these data sets in the MJO studies have been

demonstrated recently (e.g., ).

Meanwhile, analysis using global data sets in combination with the field

observations may reveal large-scale mechanisms for MJO initiation that field

observations alone are unable to. Such large-scale mechanisms include dry-air advection

and other perturbation intrusion into the tropical Indian Ocean region from the

extratropics, subsidence connected to remote convective activities, etc.

3.4 Forecast

As outlined in Section 1, the MJO influences weather and climate – both in the

Tropics and mid-latitudes – and it is gaining increasing use as a predictor in extended

range weather prediction. At the CPC, operational MJO prediction has been established

over the last few years and has benefited this area, however, anticipation of MJO event

development or demise has made operational prediction very difficult. Statistical forecast

techniques and MJO composites perform well during established MJO events. Dynamical

model predictions are also improving during these times. However, daily monitoring of

key MJO related variables, both in the atmosphere and ocean, remains the most important

and reliable method for anticipation of MJO initiation. At the current time, operational

MJO prediction is often forced to react to the development of the MJO after the fact

rather than have the ability to reliably anticipate its initiation. Moreover, there is limited

ability to reliably forecast the demise of an established MJO event.

The DYNAMO project is expected to help in this area in two ways. First, the

extensive and targeted field observations will contribute to improved documentation of

all stages of the MJO initiation process – especially focused on important MJO variables

and the hypothesized mechanisms outlined in Section 2. The benefits of this contribution

20

from DYNAMO to operational MJO prediction may be immediate as important

precursors for the development of the large-scale MJO signal may be identified more

clearly than in the past. For example, the current MJO monitoring is mainly based on the

Wheeler-Hendon (2004) MJO index, which is formed using zonal winds and OLR. It is

an excellent index to track the propagation of the MJO. But it does not provide sufficient

information about MJO initiation, especially under Scenario A. Through DYNAMO, an

MJO initiation index can be developed, which may take into account other variables in

addition to zonal winds and OLR that are critical to MJO initiation. Second, the

observational-modeling framework outlined as part of DYNAMO will accelerate

improvements in operational MJO prediction over time through extensive model

development keyed to the MJO initiation problem. These advances are likely to occur

more quickly than would be the case in the absence of DYNAMO.

An example of what may be possible through the second pathway described above is

illustrated in Fig. 6. During January 2009, CPC made a successful prediction for MJO

onset in large part from dynamical model predictions (Figure 6a-b). This was the first

time CPC made a prediction of this type and allowed for atypical lead time to users. The

subsequent observational data is shown in Fig. 6c. It is important to note that future

forecasts (not shown) produced a MJO forecast closer to the observations than what is

shown in the later days of this forecast. It is hypothesized that an external forcing from

the extratropics (East Asian cold surge – Fig. 6d) may have contributed to MJO initiation

in a manner outlined in Section 2 (Scenario A, Hypothesis V).

Figure 6 Example illustrating how a better understanding of MJO initiation will increase forecast

lead time for users. (a) GEFS MJO index forecast indicating a developing MJO signal in the

western Pacific which propagates eastward; (b) Official CPC MJO forecast for a developing MJO

and its expected impacts; (c) Subsequent observations (red circle is 5 day period of low level

wind average in lower right), and (d) Pentad low-level wind anomalies indicating a east Asian

cold surge that may have contributed to MJO initiation.

21

Further support for DYNAMO can be provided by a US CLIVAR MJO prediction

project (led by Jon Gottschalck) currently underway at NCEP/CPC, where real-time

dynamical model MJO forecasts are being made based on numerical model output from

several operational forecast centers around the world (e.g., NCEP, UKMO, ECMWF,

ABOM, CMC, JMA; Gottschalck et al. 2009)1. Such operational forecast implementation

provides practical measures for when and where MJO prediction skill is particularly

limited and model results diverge. Continuous forecast verification leads to physical

insights to factors that potentially hinder MJO prediction and to recommendations for

field campaign targets. Archived forecast products serve as a statistical base to quantify

model improvement. If needed, the real-time MJO forecasts as part of this activity from

the various international centers will be a part of operational support for the

DYNAMO/CINDY2011 field campaign and its partner program (PAC3E-SA/7SEAS and

the ONR air-sea field experiment).

4. Readiness and Program Synergy

It cannot be more timely for the US research and operations communities to make

significant contributions to the study of the MJO initiation problem by participating in

CINDY2011. First, the ongoing YOTC activities will provide experience and

organizational infrastructure for an international observation-modeling-analysis-forecast

integrated approach to tackle the problem of MJO initiation. MJO prediction has been

practiced in an organized way under the guidance of the US CLIVAR MJO Working

Group. These research-operation infrastructures will pave the road to directly connect

field observations to modeling and forecasting activities. By 2011, the IndOOS and

RAMA mooring array will be nearly completed, providing climatic background

information for the field campaign on basin- and multi-year scales. Since TOGA

COARE, observing technology has been greatly advanced to make measurement at either

unprecedented accuracy (e.g., GPS sondes, shipboard Doppler current profiles) or

relatively low cost (mixing profiles in the upper ocean). Lastly, but most importantly,

international cooperation and other US programs will be in place in late 2011 – early

2012 to provide a comprehensive suite of observations across a large region covering the

equatorial Indian Ocean, Maritime Continent, and western Pacific Ocean, as briefly

described below. The resulting synergy will make the DYNAMO field campaign much

more productive than it would be as a stand alone project. This makes late 2011 – early

2012 a critical time for DYNAMO.

Figures 7 illustrates potential DYNAMO partner programs that are being planned for

late 2011 – early 2012. Committed international participations in CINDY2011 include

Japan (50-day ship time of Mirai with a Doppler precipitation radar, radiosondes, and

surface and upper-ocean observations) and India (30-day ship time of Sagar Kanya with

radiosondes). Australian scientists are planning to join the campaign (30 day ship time of

Southern Surveyor, with radiosondes, surface and upper ocean observations). Enhanced

radiosonde observations will be conducted at Seychelles during CINDY2011, in addition

to the operational sounding launches in the region. An ONR supported field experiment on meso- and synoptic-scale air-wave-sea

1 http://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/CLIVAR/clivar_wh.shtml

22

interaction in the Indian Ocean will take place for the period of CINDY2011 (late 2011).

This field program may complement DYNAMO/CINDY2011 very well by focusing on

more detailed measurement of processes at the air-sea interface and in the boundary and

mixed layers at each side of the interface. DYNAMO/CINDY2011, on the other hand,

can provide the measurement of the large-scale context within which such detailed

processes take place. The two programs can share observing platforms, instrument and

expertise. Ultimately, the combined data set will cover multi-scale processes in the Indian

Ocean that advanced numerical models must accurately reproduce.

A proposal has been submitted to the ARM Program to conduct an enhanced

observational period of six months (AMIE). At the ARM western Pacific site on Manus

Island increased sounding (8 per day) are proposed during a six-month period embedding

the DYNAMO/CINDY2011 field campaign. Manus is a location where the MJO just

starts to regain its strength after suffering from its usual weakening over the Maritime

Continent. The combination of AMF2 and SMART C-band radar to be deployed on an

Indian Ocean island under the DYNAMO plan would form an observational package

almost identical to that at the ARM Manus site. Data collected from these two sites will

contrast same MJO events at two different stages of their life cycles. Meanwhile,

DYNAMO can interact with and benefit from the ARM modeling community, which has

considerable experience and expertise in simulations and parameterization of tropical

convection and its interaction with the large-scale circulation. They also have been

working on the modeling-observation integration.

Between the CINDY2011 site in the central equatorial Indian Ocean and the AMIE

Manus site is an observational network of Doppler radars and wind profilers over the

Indonesian Archipelagos (HARIMAU) to document the propagation of the MJO over the

Maritime Continent. HARIMAU (http://www.jamstec.go.jp/iorgc/harimau/) is a project

jointly conducted in Indonesia by Indonesian and Japanese institutes. Its objective is to

collect and diagnose observations to further physical understandings of intraseasonal

variation in terms of convective and rainfall activities over the Maritime Continent, to

help prevention from natural disasters due to extreme events, and to provide useful

information for local management and capacity building. Its observational network

consists of six sites equipped with C- and X-band Doppler radars, wind profilers, GPS

sondes, surface meteorological measurement including rain gauges. It’s first phase lasts

from April 2005 to March 2010 and its follow-on project is now being proposed. Data

from HARIMAU provide unique information of the MJO weakening over the Maritime

Continent, an unsettled problem to both MJO understanding and prediction. Integrated

observations from DYNAMO/CINDY2011, HARIMAU, and AMIE would allow some

Figure 7 Global perspective and partner programs of CINDY2011/DYNAMO. The exact site of

the ONR Air-Sea experiment has yet to be determined.

23

MJO events to be monitored at three different stages of their life cycles.

A proposed NASA interdisciplinary atmospheric sciences program to study the

interactions of pollution with regional meteorology, particularly with clouds, the Seven

SouthEast Asian Studies (7SEAS), and an intensive field campaign, the Pacific

Atmospheric Composition, Cloud, and Climate Experiment – Southeast Asia (PAC3E-

SA), will be in place over the Maritime Continent. PAC3E-SA is a NASA field program

for August-September 2011 to study the transport and vertical redistribution of

atmospheric constituents by and in proximity to convection in the Southeast Asian

region. While PAC3E-SA will have comprehensive atmospheric chemistry components,

one of its primary foci is the role of convection in pumping aerosol and evolving

boundary layer air into the free troposphere. It is anticipated that the PAC3E-SA mission

will be a multi-aircraft field campaign, augmented by surface and shipboard

measurements of aerosol and meteorology from 7SEAS. Potential interaction between

aerosol and the MJO is an open question challenging both DYNAMO and PAC3E-

SA/7SEAS. Observations taken from these programs can be highly complementary, with

data covering different longitudes featuring aerosols of different sources and

characteristics.

By joining this suite of field programs in late 2011 – early 2012, the DYNAMO

observations will be part of an integrated data set to monitor the MJO from its birthplace

in the Indian Ocean to it mature stage over the western Pacific. Such an opportunity to

capture the whole life cycle of the MJO and its interaction with the ocean, land and

aerosol as it propagates from the Indian Ocean over the Maritime Continent into the

western Pacific would probably not come again in the foreseeable future.

24

Appendix Acronym List

ABOM Australia Bureau of Meteorology

ACRF ARM Climate Research Facility

AIRS The Atmospheric Infrared Sounder

AMF2 ARM Mobile Facility 2

AMIE ACRF MJO Investigation Experiment

ARM Atmospheric Radiation Measurement

CAM Community Atmosphere Model

CAPT CCPP-ARM Parameterization Testbed

CCPP Climate Change Prediction Programs

CCS Centre for Climate Studies (Brazil)

CFS Coupled Forecast System

CINDY2011 Cooperative Indian Ocean Experiment on Intraseasonal Variability in

the Year 2011

CMC Canadian Meteorological Centre

COAMPS Coupled Ocean-Atmosphere Mesoscale Prediction System

CPC Climate Prediction Center

CRM Cloud Resolving Model

CSU Colorado State University

CTB Climate Test Bed

CWB Central Weather Bureau (Tawain)

ECMWF European Centre for Medium-Range Weather Forecasts

EDO Experimental Design Overview

EMC Environmental Modeling Center

EOL Earth Observing Laboratory

DRI Departmental Research Initiative

DOE Department of Energy

DYNAMO Dynamics of the MJO

ENSO El Niño – Southern Oscillation

ESRL Earth System Research Laboratory

FNMOC Fleet Numerical Meteorology and Oceanography Center

GDAS Global Data Assimilation System

GEFS Global Ensemble Forecast System

GEOS-5 Goddard Earth Observing System Model Version 5

GPS Global Position System

HARIMAU Hydrometeorological Array for ISV-Monsoon Automonitoring

HcGCM Hybrid coupled GCM

INDEOX The Indian Ocean Experiment

IndOOS Indian Ocean Observing System

IOD Indian Ocean Dipole

IPRC International Pacific Research Center

IROAM IPRC Regional Ocean-Atmosphere Model

ISU Iowa State University

ISV Intraseasonal Variation

JASMINE Joint Air-Sea Monsoon Investigation

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JAMSTEC Japan Agency for Marine-Earth Science and Technology

JMA Japan Meteorological Agency

IMD India Meteorological Department

JPL Jet Propulsion Laboratory

LOI Letter of Intent

MISMO Mirai Indian Ocean cruise for the Study of MJO-convection onset

MJO Madden-Julian Oscillation

MMM Mesoscale and Microscale Meteorology

NASA National Aeronautics and Space Administration

NCAR National Center for Atmospheric Research

NCEP National Center for Environmental Prediction

NOAA National Ocean and Atmosphere Administration

NOGAPS Navy's Operational Global Atmospheric Prediction System Model

NPS Naval Postgraduate School

NRCM NCAR Nested Regional Climate Model

NRL Navy Research Laboratory

NSF National Science Foundation

ONR Office of Navy Research

OSU Oregon State University

PAC3E-SA Pacific Atmospheric Composition, Cloud and Climate Experiment –

Southeast Asia

PNNL Pacific Northwest National Laboratory

PSMIP Process Study and Model Improvement Panel

RAMA Research Moored Array for African-Asian-Australian Monsoon

Analysis and Prediction

R/V Research Vessel

7SEAS The Seven SouthEast Asian Studies

SCTR Seychelles-Chagos thermocline ridge

SMART-R Shared Mobile Atmosphere Research and Teaching Radar

SPO Science Planning Overview

THORPEX The Observing System Research and Predictability Experiment

TRIO Thermocline Ridge of the Indian Ocean

TOGA COARE Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere

Research Experiment

UCSD University of California at San Diego

UH University of Hawaii

UKMO UK Met Office

UM University of Miami

UW University of Washington

UNOLS University-National Oceanographic Laboratory System

YOTC Year of Tropical Convection

WCRP World Climate Research Program

WWRP World Weather Research Program